Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

Abstract

High entropy alloys (HEAs) have excellent application prospects in catalysis because of their rich components and configuration space. In this work, we develop a Bayesian neural network (BNN) based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system. The BNN model was developed by considering six independent features of Co-Ni, Co-Rh, Co-Ru, Ni-Rh, Ni-Ru, and Rh-Ru in different shells and energies of structures as the labels. The root mean squared error of the energy predicted by BNN is 1.37 meV/atom. Moreover, the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed. We found that when the neural network is optimized to a certain extent, only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios. More importantly, we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.